TY - CHAP A1 - Schneider, Dominik A1 - Liebhart, Bernhard A1 - Endisch, Christian T1 - Adaptive Variance Estimation of Sensor Noise within a Sensor Data Fusion Framework T2 - 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC 2021) Proceedings UR - https://doi.org/10.1109/I2MTC50364.2021.9459790 Y1 - 2021 UR - https://doi.org/10.1109/I2MTC50364.2021.9459790 SN - 978-1-7281-9539-1 SN - 2642-2077 PB - IEEE CY - Piscataway ER - TY - CHAP A1 - Schneider, Dominik A1 - Endisch, Christian T1 - Robustness and reliability of model-based sensor data fusion in a lithium-ion battery system T2 - IEEE CCTA 2020, 4th IEEE Conference on Control Technology and Applications UR - https://doi.org/10.1109/CCTA41146.2020.9206336 KW - Batteries KW - Robustness KW - Parameter estimation KW - Current measurement KW - Noise measurement KW - Data models KW - Kalman filters Y1 - 2020 UR - https://doi.org/10.1109/CCTA41146.2020.9206336 SN - 978-1-7281-7140-1 SP - 685 EP - 691 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schneider, Dominik A1 - Liebhart, Bernhard A1 - Endisch, Christian T1 - Active state and parameter estimation as part of intelligent battery systems JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2021.102638 Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102638 SN - 2352-1538 VL - 2021 IS - 39 PB - Elsevier CY - Amsterdam ER - TY - CHAP A1 - Terbrack, Christoph A1 - Stöttner, Julia A1 - Endisch, Christian T1 - Operation of an externally excited synchronous machine with a hybrid multilevel inverter T2 - 2020 22nd European Conference on Power Electronics and Applications (EPE'20 ECCE Europe) UR - https://doi.org/10.23919/EPE20ECCEEurope43536.2020.9215895 KW - AC machine KW - Batteries KW - Converter circuit KW - Electrical drive KW - Electrical machine KW - High voltage power converters KW - Multilevel converters KW - Power converters for EV KW - Synchronous motor Y1 - 2020 UR - https://doi.org/10.23919/EPE20ECCEEurope43536.2020.9215895 SN - 978-9-0758-1536-8 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Hanzl, Christian A1 - Hartmann, Christoph A1 - Hölzle, Markus A1 - Liebhart, Bernhard A1 - Schmid, Michael A1 - Endisch, Christian T1 - Current commutation in a switched lithium-ion cell used in cascaded half-bridge multilevel inverters JF - IET Power Electronics N2 - In order to improve the efficiency of battery electric vehicles, the utilization of reconfigurable batteries and multilevel inverters based on low-voltage Si MOSFETs gains growing attention. Focusing on the half-bridge as basic switching unit, this paper examines voltage and current waveforms during switching. It is shown that the current commutation speed depends on various factors such as sign and amplitude of the current as well as the particular switching scenario. For instance, turning off a positive cell current takes 0.21 𝜇s while turning it on takes 0.9 𝜇s. In addition, it is revealed that depending on the switching scenario and current direction, the lithium-ion cell supports or opposes the remagnetization of the parasitic inductances and therefore the switching losses of the MOSFETs. Switching a positive load current of 125 A from active to bypass converts 276 𝜇J into heat while switching from bypass to active converts merely 32.5 𝜇J into heat. UR - https://doi.org/10.1049/pel2.12088 Y1 - 2021 UR - https://doi.org/10.1049/pel2.12088 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13364 SN - 1755-4543 VL - 14 IS - 6 SP - 1073 EP - 1088 PB - Wiley CY - Hoboken ER - TY - JOUR A1 - Schmid, Michael A1 - Gebauer, Emanuel A1 - Hanzl, Christian A1 - Endisch, Christian T1 - Active model-based fault diagnosis in reconfigurable battery systems JF - IEEE Transactions on Power Electronics UR - https://doi.org/10.1109/TPEL.2020.3012964 KW - active fault isolation KW - active hypothesis testing KW - constrained parameter estimation KW - fault tolerance KW - fuzzy clustering KW - reconfigurable battery system (RBS) Y1 - 2020 UR - https://doi.org/10.1109/TPEL.2020.3012964 SN - 1941-0107 SN - 0885-8993 VL - 36 IS - 3 SP - 2584 EP - 2597 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Schmid, Michael A1 - Kneidinger, Hans-Georg A1 - Endisch, Christian T1 - Data-driven fault diagnosis in battery systems through cross-cell monitoring JF - IEEE Sensors Journal UR - https://doi.org/10.1109/JSEN.2020.3017812 KW - Battery systems KW - data-driven fault diagnosis KW - fault isolation KW - recursive principal component analysis KW - signal processing Y1 - 2020 UR - https://doi.org/10.1109/JSEN.2020.3017812 SN - 1558-1748 SN - 1530-437X SN - 2379-9153 VL - 21 IS - 2 SP - 1829 EP - 1837 PB - IEEE CY - New York ER - TY - CHAP A1 - Kleiner, Jan A1 - Heider, Alexander A1 - Hanzl, Christian A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of an intelligent li-ion cell under vehicle conditions T2 - Proceedings: IECON 2020 The 46th Annual Conference of the IEEE Industrial Electronics Society UR - https://doi.org/10.1109/IECON43393.2020.9254336 KW - Li-ion KW - electro-thermal model KW - intelligent battery KW - reconfigurable system KW - smart cell Y1 - 2020 UR - https://doi.org/10.1109/IECON43393.2020.9254336 SN - 978-1-7281-5414-5 SN - 2577-1647 SP - 2081 EP - 2086 PB - IEEE CY - Piscataway ER - TY - JOUR A1 - Mayer, Sebastian A1 - Classen, Tobias A1 - Endisch, Christian T1 - Modular production control using deep reinforcement learning: proximal policy optimization JF - Journal of Intelligent Manufacturing N2 - EU regulations on CO2 limits and the trend of individualization are pushing the automotive industry towards greater flexibility and robustness in production. One approach to address these challenges is modular production, where workstations are decoupled by automated guided vehicles, requiring new control concepts. Modular production control aims at throughput-optimal coordination of products, workstations, and vehicles. For this np-hard problem, conventional control approaches lack in computing efficiency, do not find optimal solutions, or are not generalizable. In contrast, Deep Reinforcement Learning offers powerful and generalizable algorithms, able to deal with varying environments and high complexity. One of these algorithms is Proximal Policy Optimization, which is used in this article to address modular production control. Experiments in several modular production control settings demonstrate stable, reliable, optimal, and generalizable learning behavior. The agent successfully adapts its strategies with respect to the given problem configuration. We explain how to get to this learning behavior, especially focusing on the agent’s action, state, and reward design. UR - https://doi.org/10.1007/s10845-021-01778-z KW - modular production KW - production control KW - production scheduling KW - deep reinforcement learning KW - proximal policy optimization KW - automotive industry Y1 - 2021 UR - https://doi.org/10.1007/s10845-021-01778-z UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13092 SN - 1572-8145 VL - 32 IS - 8 SP - 2335 EP - 2351 PB - Springer Nature CY - Cham ER - TY - JOUR A1 - Theiler, Michael A1 - Endisch, Christian A1 - Lewerenz, Meinert T1 - Float Current Analysis for Fast Calendar Aging Assessment of 18650 Li(NiCoAl)O2/Graphite Cells JF - Batteries N2 - Float currents are steady-state self-discharge currents after a transient phase—caused by anode overhang, polarization, etc.—is accomplished. The float current is measured in this study with a standard test bench for five 18650 cells (Samsung 25R) at potentiostatic conditions while the temperature is changed in 5 K steps from 5 °C to 60 °C. The entire test is performed in about 100 days resulting in 12 measurement points per cell potential for an Arrhenius representation. The float current follows the Arrhenius law with an activation energy of about 60 kJ/mol. The capacity loss measured at reference condition shows a high correlation to the results of float currents analysis. In contrast to classical calendar aging tests, the performed float current analysis enables determining the aging rate with high precision down to at least 10 °C. Returning from higher temperatures to 30 °C reference temperature shows reducing float currents at 30 °C for increasing temperature steps that may originate from an hysteresis effect that has to be investigated in future publications. UR - https://doi.org/10.3390/batteries7020022 KW - 18650 KW - NCA KW - graphite KW - float current KW - self-discharge KW - calendar aging KW - Arrhenius Y1 - 2021 UR - https://doi.org/10.3390/batteries7020022 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13081 SN - 2313-0105 VL - 7 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kleiner, Jan A1 - Stuckenberger, Magdalena A1 - Komsiyska, Lidiya A1 - Endisch, Christian T1 - Advanced monitoring and prediction of the thermal state of intelligent battery cells in electric vehicles by physics-based and data-driven modeling JF - Batteries N2 - Novel intelligent battery systems are gaining importance with functional hardware on the cell level. Cell-level hardware allows for advanced battery state monitoring and thermal management, but also leads to additional thermal interactions. In this work, an electro-thermal framework for the modeling of these novel intelligent battery cells is provided. Thereby, a lumped thermal model, as well as a novel neural network, are implemented in the framework as thermal submodels. For the first time, a direct comparison of a physics-based and a data-driven thermal battery model is performed in the same framework. The models are compared in terms of temperature estimation with regard to accuracy. Both models are very well suited to represent the thermal behavior in novel intelligent battery cells. In terms of accuracy and computation time, however, the data-driven neural network approach with a Nonlinear AutoregRessive network with eXogeneous input (NARX) shows slight advantages. Finally, novel applications of temperature prediction in battery electric vehicles are presented and the applicability of the models is illustrated. Thereby, the conventional prediction of the state of power is extended by simultaneous temperature prediction. Additionally, temperature forecasting is used for pre-conditioning by advanced cooling system regulation to enable energy efficiency and fast charging. UR - https://doi.org/10.3390/batteries7020031 KW - lithium-ion battery KW - electro-thermal model KW - smart cell KW - intelligent battery KW - neural network KW - temperature prediction Y1 - 2021 UR - https://doi.org/10.3390/batteries7020031 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-13068 SN - 2313-0105 VL - 7 IS - 2 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kleiner, Jan A1 - Singh, Randeep A1 - Schmid, Michael A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Influence of heat pipe assisted terminal cooling on the thermal behavior of a large prismatic lithium-ion cell during fast charging in electric vehicles JF - Applied Thermal Engineering UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 KW - Lithium-ion battery KW - Battery modeling KW - Electro-thermal model KW - Fast charging KW - Terminal cooling KW - Heat pipe Y1 - 2020 UR - https://doi.org/10.1016/j.applthermaleng.2020.116328 SN - 1359-4311 VL - 2021 IS - 188 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Kleiner, Jan A1 - Lechermann, Lorenz A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal behavior of intelligent automotive lithium-ion batteries BT - operating strategies for adaptive thermal balancing by reconfiguration JF - Journal of energy storage UR - https://doi.org/10.1016/j.est.2021.102686 KW - intelligent battery KW - thermal management KW - balancing KW - cell-to-cell variations KW - inhomogeneities KW - thermal modeling Y1 - 2021 UR - https://doi.org/10.1016/j.est.2021.102686 SN - 2352-1538 VL - 2021 IS - 40 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Eriksson, Lars A1 - Thomasson, Andreas A1 - Ekberg, Kristoffer A1 - Reig, Alberto A1 - Eifert, Mark A1 - Donatantonio, Fabrizio A1 - D'Amato, Antonio A1 - Arsie, Ivan A1 - Pianese, Cesare A1 - Otta, Pavel A1 - Held, Manne A1 - Vögele, Ulrich A1 - Endisch, Christian T1 - Look-ahead controls of heavy duty trucks on open roads — six benchmark solutions JF - Control Engineering Practice UR - https://doi.org/10.1016/j.conengprac.2018.10.014 KW - Engine model KW - Driveline model KW - Vehicle model KW - Control design KW - Optimal control Y1 - 2018 UR - https://doi.org/10.1016/j.conengprac.2018.10.014 SN - 1873-6939 VL - 2019 IS - 83 SP - 45 EP - 66 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schneider, Dominik A1 - Vögele, Ulrich A1 - Endisch, Christian T1 - Model-based sensor data fusion of quasi-redundant voltage and current measurements in a lithium-ion battery module JF - Journal of Power Sources UR - https://doi.org/10.1016/j.jpowsour.2019.227156 KW - Battery model KW - Current sensor KW - Dual kalman filter KW - Sensor data fusion KW - State estimation KW - Weighted least squares Y1 - 2019 UR - https://doi.org/10.1016/j.jpowsour.2019.227156 SN - 1873-2755 VL - 2019 IS - 440 PB - Elsevier CY - New York ER - TY - JOUR A1 - Liebhart, Bernhard A1 - Komsiyska, Lidiya A1 - Endisch, Christian T1 - Passive impedance spectroscopy for monitoring lithium-ion battery cells during vehicle operation JF - Journal of Power Sources UR - https://doi.org/10.1016/j.jpowsour.2019.227297 KW - (Nonparametric) system identification KW - (Passive) impedance spectroscopy KW - Data-driven battery parameter estimation KW - Single-cell BMS Y1 - 2019 UR - https://doi.org/10.1016/j.jpowsour.2019.227297 SN - 1873-2755 VL - 2020 IS - 449 PB - Elsevier CY - New York ER - TY - JOUR A1 - Lewerenz, Meinert A1 - Rahe, Christiane A1 - Fuchs, Georg A1 - Endisch, Christian A1 - Sauer, Dirk Uwe T1 - Evaluation of shallow cycling on two types of uncompressed automotive Li(Ni1/3Mn1/3Co1/3)O2-Graphite pouch cells JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2020.101529 KW - NMC KW - Compression KW - Cyclic aging tests KW - Differential voltage analysis KW - Post-mortem analysis KW - Irreversible aging KW - Anode overhang KW - Homogeneity of lithium distribution Y1 - 2020 UR - https://doi.org/10.1016/j.est.2020.101529 SN - 2352-1538 VL - 2020 IS - 30 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Schmid, Michael A1 - Vögele, Ulrich A1 - Endisch, Christian T1 - A novel matrix-vector-based framework for modeling and simulation of electric vehicle battery packs JF - Journal of Energy Storage UR - https://doi.org/10.1016/j.est.2020.101736 KW - Electric vehicle KW - Lithium-ion battery pack KW - Control-oriented modeling KW - Electro-thermal model KW - Fault diagnosis KW - Performance analysis Y1 - 2020 UR - https://doi.org/10.1016/j.est.2020.101736 SN - 2352-1538 VL - 2020 IS - 32 PB - Elsevier CY - Amsterdam ER - TY - JOUR A1 - Liebhart, Bernhard A1 - Satzke, Martin A1 - Komsiyska, Lidiya A1 - Endisch, Christian T1 - Application of nonlinear impedance spectroscopy for the diagnosis of lithium-ion battery cells under various operating conditions JF - Journal of Power Sources UR - https://doi.org/10.1016/j.jpowsour.2020.228673 KW - Nonlinear impedance spectroscopy KW - Mechanical pressure KW - Frequency response functions KW - Volterra kernels Y1 - 2020 UR - https://doi.org/10.1016/j.jpowsour.2020.228673 SN - 1873-2755 VL - 2020 IS - 480 PB - Elsevier CY - New York ER - TY - JOUR A1 - Komsiyska, Lidiya A1 - Buchberger, Tobias A1 - Diehl, Simon A1 - Ehrensberger, Moritz A1 - Hanzl, Christian A1 - Hartmann, Christoph A1 - Hölzle, Markus A1 - Kleiner, Jan A1 - Lewerenz, Meinert A1 - Liebhart, Bernhard A1 - Schmid, Michael A1 - Schneider, Dominik A1 - Speer, Sascha A1 - Stöttner, Julia A1 - Terbrack, Christoph A1 - Hinterberger, Michael A1 - Endisch, Christian T1 - Critical review of intelligent battery systems BT - challenges, implementation, and potential for electric vehicles JF - Energies N2 - This review provides an overview of new strategies to address the current challenges of automotive battery systems: Intelligent Battery Systems. They have the potential to make battery systems more performant and future-proof for coming generations of electric vehicles. The essential features of Intelligent Battery Systems are the accurate and robust determination of cell individual states and the ability to control the current of each cell by reconfiguration. They enable high-level functions like fault diagnostics, multi-objective balancing strategies, multilevel inverters, and hybrid energy storage systems. State of the art and recent advances in these topics are compiled and critically discussed in this article. A comprising, critical discussion of the implementation aspects of Intelligent Battery Systems complements the review. We touch on sensing, battery topologies and management, switching elements, communication architecture, and impact on the single-cell. This review contributes to transferring the best technologies from research to product development. UR - https://doi.org/10.3390/en14185989 KW - smart battery KW - intelligent battery system KW - advanced monitoring KW - reconfigurable battery KW - battery management system KW - electric vehicle Y1 - 2021 UR - https://doi.org/10.3390/en14185989 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-10132 SN - 1996-1073 VL - 14 IS - 18 PB - MDPI CY - Basel ER - TY - JOUR A1 - Kleiner, Jan A1 - Komsiyska, Lidiya A1 - Elger, Gordon A1 - Endisch, Christian T1 - Thermal modelling of a prismatic lithium-ion cell in a battery electric vehicle environment BT - influences of the experimental validation setup JF - Energies N2 - In electric vehicles with lithium-ion battery systems, the temperature of the battery cells has a great impact on performance, safety, and lifetime. Therefore, developing thermal models of lithium-ion batteries to predict and investigate the temperature development and its impact is crucial. Commonly, models are validated with experimental data to ensure correct model behaviour. However, influences of experimental setups or comprehensive validation concepts are often not considered, especially for the use case of prismatic cells in a battery electric vehicle. In this work, a 3D electro–thermal model is developed and experimentally validated to predict the cell’s temperature behaviour for a single prismatic cell under battery electric vehicle (BEV) boundary conditions. One focus is on the development of a single cell’s experimental setup and the investigation of the commonly neglected influences of an experimental setup on the cell’s thermal behaviour. Furthermore, a detailed validation is performed for the laboratory BEV scenario for spatially resolved temperatures and heat generation. For validation, static and dynamic loads are considered as well as the detected experimental influences. The validated model is used to predict the temperature within the cell in the BEV application for constant current and Worldwide harmonized Light vehicles Test Procedure (WLTP) load profile. UR - https://doi.org/10.3390/en13010062 KW - Lithium-Ionen-Batterie KW - Elektrofahrzeug Y1 - 2019 UR - https://doi.org/10.3390/en13010062 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-7532 SN - 1996-1073 VL - 13 IS - 1 PB - MDPI CY - Basel ER -